International Journal of Innovative Research in Advanced Engineering (IJIRAE) Volume 1 Issue 10 (November 2014)
|
|
- Donna Maxwell
- 5 years ago
- Views:
Transcription
1 Technique for Suppression Random and Physiological Noise Components in Functional Magnetic Resonance Imaging Data Mawia Ahmed Hassan* Biomedical Engineering Department, Sudan University of Science & Technology, Khartoum, Sudan ( Abstract Magnetic Resonance Imaging (MRI) uses Radio waves and strong magnetic field rather than X-ray to provide clear and detailed pictures of internal organs and tissues. Functional Magnetic Resonance Imaging (fmri) is a non-invasive brain imaging technique, which developed in the early 1990's, for determining which parts of the brain are activated by different types of physical sensation or activity, such as sight, sound, or the movement of subject's fingers, and detecting the corresponding increase in blood flow. Denoising procedure for functional Magnetic Resonance Imaging (fmri) is introduced in this paper. The noises are classified into random noise components and physiological baseline fluctuation components. The proposed technique based on threshold the Fourier spectrum of the output response to remove any frequencies less than the fundamental frequency and harmonics of the true activation, which it is periodic. The proposed work was conducted using computer simulations data (block design), real baseline data with simulated activation patterns, as well as real data from event-related fmri study on a normal human volunteer. The results show that, the new technique is suppressing both random and physiological noise components while preserving the true activation in the signal from the acquired data in a simple and efficient way. This allows the new method to overcome the limitation of previous techniques while maintaining a robust performance and suggests its value as a useful preprocessing step for fmri data analysis. Keywords fmri; Random noise; Physiological noise; Power spectrum. I. INTRODUCTION Functional Magnetic Resonance Imaging (fmri) is a non-invasive brain imaging technique, which is developed in the early 1990's [1] for determining which parts of the brain are activated by different types of physical sensation or activity, such as sight, sound, or the movement of subject's fingers, and detecting the corresponding increase in blood flow. To observe these hemodynamic changes, rapid acquisition of a series of brain images is performed. The sequence of images is analyzed to detect such changes and the result is expressed in the form of a map of the activated regions in the brain [2]. In the majority of reported functional human brain mapping studies using fmri, blocks of baseline and activation images are scanned periodically. Typically, a number of frames are acquired while the subject is at rest or under some baseline condition, this is followed by a number of activation frames during which the subject is receiving a sensory stimulus or performing a specified motor or cognitive task [3]. This pattern is repeated in order to improve the Signal-to-noise ratio (SNR). New advances that improve the temporal resolution of fmri called single trial or event-related fmri (ER-fMRI). In this new design, the subject receives a short stimulus or performs a single instance task while the resultant transient response is measured [4]. Event-related fmri offers many advantages over block design that include versatility, investigation of trial-to-trial variations, and extraction of epoch-dependent information and direct adaptation of the methods used for Evoked-response potential (ERP) to fmri [5]. One significant limitation in Event-related functional Magnetic Resonance Imaging (ER-fMRI) is the degradation in signal-to-noise ratio (SNR) due to the transient nature of the response [5]. Several methods of data analysis have been used to process the fmri raw data. Retrospective estimation and correction of physiological artifacts in fmri by direct extraction of physiological activity from MR data [6]: A physiological artifact reduction method based on extracting respiratory motion and cardiac pulsation directly from functional MR data is described. Activation detection in functional MRI using subspace modeling and maximum likelihood estimation [3]: Statistical method for detecting activated pixels in functional MRI (fmri) data. In this method, the fmri time series measured at each pixel is modeled as the sum of a response signal which arises due to the experimentally controlled activation-baseline pattern, a nuisance component representing effects of no interest, and Gaussian white noise. Anisotropic 2-D and 3-D averaging of fmri signals [8]: Method for denoising functional MRI temporal signals is presented in this note. The method is based on progressively enhancing the temporal signal by means of adaptive anisotropic spatial averaging. Detection of cortical activation during averaged single trials of a cognitive task using functional magnetic resonance imaging [9]: Here methods for acquiring fmri data from single trials of a cognitive task. Wavelet transform-based Wiener filtering of event-related fmri data [10]: The advent of event-related functional magnetic resonance imaging (fmri) has resulted in many exciting studies that have exploited its unique capability. Adaptive Denoising of Event-Related Functional Magnetic Resonance Imaging Data Using Spectral Subtraction [5]: A signalpreserving technique for noise suppression in event-related functional magnetic resonance imaging (fmri) data based on spectral subtraction. Combined with subsequent regression of physiological confounders is represented in [13]. Using a combination of high speed magnetic resonance inverse imaging (InI) and digital filtering is described in [14]. Data-driven noise correction, termed APPLECOR (for Affine Parameterization of Physiological Large-scale Error Correction) is modelled spatially-common physiological noise as the linear combination of an additive term and a mean-dependent multiplicative term, Page -425
2 and then estimates and removes these components [15]. The above methods which proposed to suppress physiological noise including the use of harmonic model [6] and noise subspace characterization [3]. Others attempted to use different strategies to suppress the effect of random noise in the analysis using finite impulse response (FIR) filter modeling [7], smart spatial averaging [8], inter-epoch averaging [9], Wiener filtering [10], and Canonical Correlation Analysis [11] [12]. These techniques suffer from at least one of the following limitations: the need for extended data acquisition for inter-epoch averaging that might not be practical, assumption of limited epoch-to-epoch variability, dependence on assumptions about the signal characteristics to build the denoising filter, and the inability to use conventional statistical detection approaches because of correlated noise among spatial and/or temporal points. Adaptive Denoising of Event-Related Functional Magnetic Resonance Imaging Data Using Spectral Subtraction [5], the limitations of the technique include two parts, namely, the noise in phase component, and the effect of the clipping in the subtracted power spectrum. All these assumption are not always valid especially when we consider both physiological and random components of noise. Therefore, we propose a denoising strategy that does not have the above limitations and suggest it would be rather useful in the clinical practice. The proposed technique is based on threshold the Fourier spectrum of the output response take into account physiological and random components noise while preserving the true activation in the signal. The new technique is tested using computer simulations (block design) as well, real data (ER-fMRI experiments). II. METHODOLOGY When examining the BOLD response we often look at a system as a linear system composed of several subsystems (Fig 1). It implies response from short stimuli should predict responses to longer stimuli. System Simulation Neuronal Processes Metabolic Processes Vascular Processes MRI Physics BOLD Signal Fig 1 BOLD response system The technique strategy steps are: 1. The fmri temporal signal can be modeled as the Linear combination system, which are the summation of the true activation signal s(t), a physiological baseline fluctuation component, and a random noise d(t) component (Linear combination system) as in equation 1: Where y(t) is fmri temporal signal, s(t) is true activation signal, and d(t) representing the sum of physiological and random noise parts. 2. Compute the Fourier transform of the linear combination system, In frequency domain, we have (equation 2): 2 2 Here, Y( ) is fmri temporal signal, S( ) is true activation signal, and D( ) representing the sum of physiological and random noise parts in the frequency domain. 3. Visualizing the Fourier transform with zero frequency components in the middle of the spectrum by multiply the input signal by (-1) t. 4. The spectral of the physiological and random noise centralized in low frequency overlapped with that of the true activation. 5. We chose DC frequency and the first three harmonics less than DC frequency, implies the harmonics of true activation that is [16].as in equation 3 and Page -426
3 Where, T is time period, is the Fourier series coefficients (, k=0, ±1, ±2, ±3, ) and is fundamental frequency. 6. We chose then threshold or magnitude (a) less than the 3 rd harmonic magnitude chose from the visualizing Fourier transform (ellipse in black the Fig.2 a) to remove random al noise components (equation 5). 5 Where, is the magnitude of the 3 rd harmonic and To reduce the physiological noise we set the value of the magnitude of d to zero (the ellipse in blue in the Fig.2 b) where 0 < d < k-r (0 is the Dc frequency, k is the fundamental frequency and r 0) 8. The remaining is the fundamental frequency and harmonics of the true activation signal. 9. Then Reconstruct the resulting signal using real part of inverse Fourier transformation. Fig. 2.a and b is shown the proposed strategy. (a) (b) Fig 2 Noise reduction Technique. (a) Complete proposed strategy, (b) Magnification of physiological noise components in (a). III. RESULTS A. The data The proposed technique was verified using computer simulations as well as actual data from a human volunteer: (a) Page -427
4 (b) (c) (d) (e) Fig 3 The computer simulations (block design): (a) block design activation signal; (b) The baseline data; (c) shifted computer simulations signal; (d) Variation baseline data; (e) Computer simulations signal with baseline variation. B. The computer simulation The computer simulations were performed by Computer generated block design activation signal was added to an actual baseline set (Fig 3).The baseline data were collected on a healthy human volunteer using an EPI sequence (TE/TR=25/500ms, Matrix=64x64, field of view (FOV=20cmx20cm, slice thickness=5mm, 640 images) on a Siemens Magnetom Vision 1.5 T clinical scanner. The number of epochs was 8 or 10 and the length of each epoch was 64. (a) Page -428
5 (b) (c) (d) Fig 4 The actual data: (a) and (b) activated pixel with in time course length 512(8 epochs) with baseline variation; (c) activated pixel with in time course length 512(8 epochs) (d) activated pixel with in time course length 256(4 epochs). C. The actual data The actual data were obtained from an ER-fMRI study (Fig 4) performed on a normal human volunteer using a Siemens 1.5 T Magnetom Vision clinical scanner [10]. In this study, an oblique slice through the motor and the visual cortices was imaged using a T2* weighted EPI sequence ((TE/TR=60/300ms, flip angle=55, matrix=64x64, FOV=22cmx22cm, slice thickness=5mm). The subject performed rapid finger movement.the study consists of 32 epochs with 64 images per epoch. Temporal data from only 4 or 8 epochs of pixels in both the motor and visual cortices are used. D. Result from simulated data The result of applying the new technique to process computer simulated fmri data, compared to classical spectral subtraction denoising [5]. The result of applying the new technique to process computer simulated fmri data are shown in Fig.5. As can be observed in Fig.5 when used simulation without baseline variation the noise in the data was eliminated clearly in the output, compared to the classical parametric spectrum subtraction denoising which eliminated amount of random noise. Also, the difference between simulated and processed signal appear to have no signal components. E. Result from Real data The result of applying the new technique to process real fmri data from activated pixel with in time course length 512 and 256 points (i.e., correspond to 8 or 4 epochs). The results compared to the classical spectral subtraction denoising (Fig.6), (Fig.7), (Fig.8) and (Fig.9).In Fig.6 when used actual data for pixel time course containing neuronal activation (length = 512 points) with baseline variations the process eliminated the random and physiological baseline variations and we used in Fig.7 acud baseline variations the results look dramatically improved compared to the original. In fact, the process removed the random noise and baseline variation. We emphasized that by the difference between original and processed signal, compared to the classical parametric spectrum subtraction denoising which remove amount of the random noise, while keeping the baseline variation. Page -429
6 Fig 5 Result from simulation without baseline variation. In Fig. 8 when used actual data for pixel time course containing neuronal activation (length = 512 points) and Fig. 9 actual data for pixel time course containing neuronal activation (length = 256 points) without baseline variations, as can be shown, the results of proposed method appear much improved from the original. Moreover, if we compared the results of the new technique to that of the classical parametric spectrum subtraction, we observe an improved noise suppression that is evident in the shown difference signal. Fig 6 Results from actual data for pixel time course containing neuronal activation (length = 512 points) with baseline variations. Page -430
7 Fig 7: Results from actual data for pixel time course containing neuronal activation (length = 512 points) with a cud baseline variations. Fig 8 Results from actual data for pixel time course containing neuronal activation (length = 512 points) without baseline variations. Page -431
8 Fig 9 Results from actual data for pixel time course containing neuronal activation (length = 256 points) without baseline variations. In Figs. 10 is shows the Fourier spectrum of original, denoising, and classical spectrum subtraction. As can be shown the peaks corresponding to the fundamental frequency and harmonics of the activation signal are still present in the Fourier spectrum. Fig 10: Fourier spectrum of signals in Fig.5 Page -432
9 IV. DISCUSSIONS The results show that, the new technique is appearing much improved from the original. Moreover, if we compared the results of the new technique to that of the classical parametric spectrum subtraction, we observe an improved noise suppression that is evident in the shown difference signal. The proposed algorithm was able to effectively suppress the baseline variations results from physiological noise. Moreover, when the two results were subtracted, the trend of the physiological noise was very clear. As can be shown the peaks corresponding to the fundamental frequency and harmonics of the activation signal are still present in the Fourier spectrum, emphasized that by the difference between original and denoising Fourier spectrum compared to classical parametric spectrum subtraction denoising, which contained noisy peaks. V. CONCLUSIONS The new technique was suppressing both random and physiological noise components while preserving the true activation in the signal from the acquired data in a simple and efficient way. This allows the new method to overcome the limitation of previous techniques while maintaining a robust performance and suggests its value as a useful preprocessing step for fmri data analysis. This method avoids the shift in phase of the hemodynamic response, which is very valuable feature. This technique provides control over noise removal using a threshold. This allows the user to customize its use to specific data analysis his/her choice. REFERENCES [1] Ogawa S, Menton S, Tank W, Functional brain mapping by blood oxygen level dependent contrast, Biophysical Journal.64: , [2] Orrison W, Lewine D, Sanders A, Hartshorne F, Functional Brain Imaging, St. Louis, MO: Mosby-Year Book, [3] Ardekani A, Kershaw J, Kashikura K, and Kanno I, Activation detection in functional MRI using subspace modeling and maximum likelihood estimation, IEEE Trans. Med. Imag. 18: , [4] Buckner L, Bandettini A, O Craven M, Savoy L, Petersen E, Raichle M, Rosen R,"Detection of cortical activation during averaged single trials of a cognitive task using functional magnetic resonance imaging," Proc. Nat. Acad. Sci. USA, vol. 93: , Fig.11 Fourier spectrum of signals in Fig.6 [5] Yasser M. Kadah," Adaptive denoising of ER-fMRI data using spectrum subtraction," IEEE Trans. Biomedical Engineering, vol.51: , [6] Le H and Hu X," Retrospective estimation and correction of physiological artifacts in fmri by direct extraction of physiological activity from MR data," Magn. Reson. Med., vol. 35: , [7] Goutte C, Nielsen A, Hansen K,"Modeling the haemodynamic response in fmri using smooth FIR filters," IEEE Trans. Med. Imag., vol. 19: , [8] Sole S, Ngan S, Sapiro G, Hu X, Lopez A," Anisotropic 2-D and 3-D averaging of fmri signals," IEEE Trans. Med. Imag., vol. 20: 86 93, Page -433
10 [9] LaConte M, Ngan S, Hu X," Wavelet transform-based Wiener filtering of event-related fmri data," Magn. Reson. Med., vol. 44: , [10] Ola F, Jonny C, Peter L, Magnus B, and Hans K," Detection of Neural Activity in Functional MRI Using Canonical Correlation Analysis," Magn Reson Med 45: , [11] Yadong L, Dewen H, Zongtan Z, Hui S and Xiang W," FMRI Noise Reduction Based on Canonical Correlation Analysis and Surrogate Test," IEEE Trans. Selected Topics in Signal Processing, vol.2 : , [12] Daniel K, Seehafer U, Chrystelle P, Dirk W, Mathias H," Functional connectivity in the rat at 11.7 T: Impact of physiological noise in resting state fmri," Elsevier. NeuroImage, vol.54 : , [13] Fa-Hsuan L, Aapo N, Thomas W, Jonathan P, Thomas Z, Fu-Nien W, John B," Physiological noise reduction using volumetric functional magnetic resonance inverse imaging," Neuroscience Human Brain Mapping, vol.33: ,2012. [14] Michael M, Kim P, and Catie C," A novel approach for global noise reduction in resting-state fmri: APPLECOR," Elsevier. NeuroImage. vol.64: 19-31, [15] Alan O, Alan W and Ian Y, Signals and systems, Prentice-hall, Inc., Englewood Cliffs, New Jersey, Page -434
Event-Related fmri and the Hemodynamic Response
Human Brain Mapping 6:373 377(1998) Event-Related fmri and the Hemodynamic Response Randy L. Buckner 1,2,3 * 1 Departments of Psychology, Anatomy and Neurobiology, and Radiology, Washington University,
More informationSupplementary information Detailed Materials and Methods
Supplementary information Detailed Materials and Methods Subjects The experiment included twelve subjects: ten sighted subjects and two blind. Five of the ten sighted subjects were expert users of a visual-to-auditory
More informationIntroduction to Functional MRI
Introduction to Functional MRI Douglas C. Noll Department of Biomedical Engineering Functional MRI Laboratory University of Michigan Outline Brief overview of physiology and physics of BOLD fmri Background
More informationHST 583 fmri DATA ANALYSIS AND ACQUISITION
HST 583 fmri DATA ANALYSIS AND ACQUISITION Neural Signal Processing for Functional Neuroimaging Neuroscience Statistics Research Laboratory Massachusetts General Hospital Harvard Medical School/MIT Division
More informationFunctional MRI Mapping Cognition
Outline Functional MRI Mapping Cognition Michael A. Yassa, B.A. Division of Psychiatric Neuro-imaging Psychiatry and Behavioral Sciences Johns Hopkins School of Medicine Why fmri? fmri - How it works Research
More informationBiomedical Imaging: Course syllabus
Biomedical Imaging: Course syllabus Dr. Felipe Orihuela Espina Term: Spring 2015 Table of Contents Description... 1 Objectives... 1 Skills and Abilities... 2 Notes... 2 Prerequisites... 2 Evaluation and
More informationClassification and Statistical Analysis of Auditory FMRI Data Using Linear Discriminative Analysis and Quadratic Discriminative Analysis
International Journal of Innovative Research in Computer Science & Technology (IJIRCST) ISSN: 2347-5552, Volume-2, Issue-6, November-2014 Classification and Statistical Analysis of Auditory FMRI Data Using
More informationInverse problems in functional brain imaging Identification of the hemodynamic response in fmri
Inverse problems in functional brain imaging Identification of the hemodynamic response in fmri Ph. Ciuciu1,2 philippe.ciuciu@cea.fr 1: CEA/NeuroSpin/LNAO May 7, 2010 www.lnao.fr 2: IFR49 GDR -ISIS Spring
More informationIntroduction to Computational Neuroscience
Introduction to Computational Neuroscience Lecture 10: Brain-Computer Interfaces Ilya Kuzovkin So Far Stimulus So Far So Far Stimulus What are the neuroimaging techniques you know about? Stimulus So Far
More informationFunctional Elements and Networks in fmri
Functional Elements and Networks in fmri Jarkko Ylipaavalniemi 1, Eerika Savia 1,2, Ricardo Vigário 1 and Samuel Kaski 1,2 1- Helsinki University of Technology - Adaptive Informatics Research Centre 2-
More informationInvestigations in Resting State Connectivity. Overview
Investigations in Resting State Connectivity Scott FMRI Laboratory Overview Introduction Functional connectivity explorations Dynamic change (motor fatigue) Neurological change (Asperger s Disorder, depression)
More informationTemporal preprocessing of fmri data
Temporal preprocessing of fmri data Blaise Frederick, Ph.D. McLean Hospital Brain Imaging Center Scope fmri data contains temporal noise and acquisition artifacts that complicate the interpretation of
More informationPHYSICS OF MRI ACQUISITION. Alternatives to BOLD for fmri
PHYSICS OF MRI ACQUISITION Quick Review for fmri HST-583, Fall 2002 HST.583: Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Harvard-MIT Division of Health Sciences and Technology
More informationSupplementary Information Methods Subjects The study was comprised of 84 chronic pain patients with either chronic back pain (CBP) or osteoarthritis
Supplementary Information Methods Subjects The study was comprised of 84 chronic pain patients with either chronic back pain (CBP) or osteoarthritis (OA). All subjects provided informed consent to procedures
More informationRemoval of Nuisance Signal from Sparsely Sampled 1 H-MRSI Data Using Physics-based Spectral Bases
Removal of Nuisance Signal from Sparsely Sampled 1 H-MRSI Data Using Physics-based Spectral Bases Qiang Ning, Chao Ma, Fan Lam, Bryan Clifford, Zhi-Pei Liang November 11, 2015 1 Synopsis A novel nuisance
More informationResting-State functional Connectivity MRI (fcmri) NeuroImaging
Resting-State functional Connectivity MRI (fcmri) NeuroImaging Randy L. Buckner et. at., The Brain s Default Network: Anatomy, Function, and Relevance to Disease, Ann. N. Y. Acad. Sci. 1124: 1-38 (2008)
More informationDesign Optimization. SBIRC/SINAPSE University of Edinburgh
Design Optimization Cyril Pernet,, PhD SBIRC/SINAPSE University of Edinburgh fmri designs: overview Introduction: fmri noise fmri design types: blocked designs event-related designs mixed design adaptation
More informationFrequency Tracking: LMS and RLS Applied to Speech Formant Estimation
Aldebaro Klautau - http://speech.ucsd.edu/aldebaro - 2/3/. Page. Frequency Tracking: LMS and RLS Applied to Speech Formant Estimation ) Introduction Several speech processing algorithms assume the signal
More informationIncorporation of Imaging-Based Functional Assessment Procedures into the DICOM Standard Draft version 0.1 7/27/2011
Incorporation of Imaging-Based Functional Assessment Procedures into the DICOM Standard Draft version 0.1 7/27/2011 I. Purpose Drawing from the profile development of the QIBA-fMRI Technical Committee,
More informationSpeech Enhancement Based on Spectral Subtraction Involving Magnitude and Phase Components
Speech Enhancement Based on Spectral Subtraction Involving Magnitude and Phase Components Miss Bhagat Nikita 1, Miss Chavan Prajakta 2, Miss Dhaigude Priyanka 3, Miss Ingole Nisha 4, Mr Ranaware Amarsinh
More informationExperimental design. Experimental design. Experimental design. Guido van Wingen Department of Psychiatry Academic Medical Center
Experimental design Guido van Wingen Department of Psychiatry Academic Medical Center guidovanwingen@gmail.com Experimental design Experimental design Image time-series Spatial filter Design matrix Statistical
More informationExperimental Design. Outline. Outline. A very simple experiment. Activation for movement versus rest
Experimental Design Kate Watkins Department of Experimental Psychology University of Oxford With thanks to: Heidi Johansen-Berg Joe Devlin Outline Choices for experimental paradigm Subtraction / hierarchical
More informationFIR filter bank design for Audiogram Matching
FIR filter bank design for Audiogram Matching Shobhit Kumar Nema, Mr. Amit Pathak,Professor M.Tech, Digital communication,srist,jabalpur,india, shobhit.nema@gmail.com Dept.of Electronics & communication,srist,jabalpur,india,
More informationTable 1. Summary of PET and fmri Methods. What is imaged PET fmri BOLD (T2*) Regional brain activation. Blood flow ( 15 O) Arterial spin tagging (AST)
Table 1 Summary of PET and fmri Methods What is imaged PET fmri Brain structure Regional brain activation Anatomical connectivity Receptor binding and regional chemical distribution Blood flow ( 15 O)
More informationFunctional Magnetic Resonance Imaging
Todd Parrish, Ph.D. Director of the Center for Advanced MRI Director of MR Neuroimaging Research Associate Professor Department of Radiology Northwestern University toddp@northwestern.edu Functional Magnetic
More informationBOLD signal compartmentalization based on the apparent diffusion coefficient
Magnetic Resonance Imaging 20 (2002) 521 525 BOLD signal compartmentalization based on the apparent diffusion coefficient Allen W. Song a,b *, Harlan Fichtenholtz b, Marty Woldorff b a Brain Imaging and
More informationEARLY STAGE DIAGNOSIS OF LUNG CANCER USING CT-SCAN IMAGES BASED ON CELLULAR LEARNING AUTOMATE
EARLY STAGE DIAGNOSIS OF LUNG CANCER USING CT-SCAN IMAGES BASED ON CELLULAR LEARNING AUTOMATE SAKTHI NEELA.P.K Department of M.E (Medical electronics) Sengunthar College of engineering Namakkal, Tamilnadu,
More informationBrain Tumor Segmentation of Noisy MRI Images using Anisotropic Diffusion Filter
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 7, July 2014, pg.744
More informationPCA Enhanced Kalman Filter for ECG Denoising
IOSR Journal of Electronics & Communication Engineering (IOSR-JECE) ISSN(e) : 2278-1684 ISSN(p) : 2320-334X, PP 06-13 www.iosrjournals.org PCA Enhanced Kalman Filter for ECG Denoising Febina Ikbal 1, Prof.M.Mathurakani
More informationSUPPRESSION OF MUSICAL NOISE IN ENHANCED SPEECH USING PRE-IMAGE ITERATIONS. Christina Leitner and Franz Pernkopf
2th European Signal Processing Conference (EUSIPCO 212) Bucharest, Romania, August 27-31, 212 SUPPRESSION OF MUSICAL NOISE IN ENHANCED SPEECH USING PRE-IMAGE ITERATIONS Christina Leitner and Franz Pernkopf
More informationOutline. Biological Psychology: Research Methods. Dr. Katherine Mickley Steinmetz
Biological Psychology: Research Methods Dr. Katherine Mickley Steinmetz Outline Neuroscience Methods Histology Electrophysiological Recordings Lesion Neuroimaging Neuroanatomy Histology: Brain structure
More informationTemporal preprocessing of fmri data
Temporal preprocessing of fmri data Blaise Frederick, Ph.D., Yunjie Tong, Ph.D. McLean Hospital Brain Imaging Center Scope This talk will summarize the sources and characteristics of unwanted temporal
More informationFREQUENCY DOMAIN HYBRID INDEPENDENT COMPONENT ANALYSIS OF FUNCTIONAL MAGNETIC RESONANCE IMAGING DATA
FREQUENCY DOMAIN HYBRID INDEPENDENT COMPONENT ANALYSIS OF FUNCTIONAL MAGNETIC RESONANCE IMAGING DATA J.D. Carew, V.M. Haughton, C.H. Moritz, B.P. Rogers, E.V. Nordheim, and M.E. Meyerand Departments of
More informationSum of Neurally Distinct Stimulus- and Task-Related Components.
SUPPLEMENTARY MATERIAL for Cardoso et al. 22 The Neuroimaging Signal is a Linear Sum of Neurally Distinct Stimulus- and Task-Related Components. : Appendix: Homogeneous Linear ( Null ) and Modified Linear
More informationCausality from fmri?
Causality from fmri? Olivier David, PhD Brain Function and Neuromodulation, Joseph Fourier University Olivier.David@inserm.fr Grenoble Brain Connectivity Course Yes! Experiments (from 2003 on) Friston
More informationBrain Computer Interface. Mina Mikhail
Brain Computer Interface Mina Mikhail minamohebn@gmail.com Introduction Ways for controlling computers Keyboard Mouse Voice Gestures Ways for communicating with people Talking Writing Gestures Problem
More informationDATA MANAGEMENT & TYPES OF ANALYSES OFTEN USED. Dennis L. Molfese University of Nebraska - Lincoln
DATA MANAGEMENT & TYPES OF ANALYSES OFTEN USED Dennis L. Molfese University of Nebraska - Lincoln 1 DATA MANAGEMENT Backups Storage Identification Analyses 2 Data Analysis Pre-processing Statistical Analysis
More informationReading Assignments: Lecture 18: Visual Pre-Processing. Chapters TMB Brain Theory and Artificial Intelligence
Brain Theory and Artificial Intelligence Lecture 18: Visual Pre-Processing. Reading Assignments: Chapters TMB2 3.3. 1 Low-Level Processing Remember: Vision as a change in representation. At the low-level,
More informationSatoru Hiwa, 1 Kenya Hanawa, 2 Ryota Tamura, 2 Keisuke Hachisuka, 3 and Tomoyuki Hiroyasu Introduction
Computational Intelligence and Neuroscience Volume 216, Article ID 1841945, 9 pages http://dx.doi.org/1.1155/216/1841945 Research Article Analyzing Brain Functions by Subject Classification of Functional
More informationExperimental design of fmri studies
Experimental design of fmri studies Kerstin Preuschoff Computational Neuroscience Lab, EPFL LREN SPM Course Lausanne April 10, 2013 With many thanks for slides & images to: Rik Henson Christian Ruff Sandra
More informationDiscrete Signal Processing
1 Discrete Signal Processing C.M. Liu Perceptual Lab, College of Computer Science National Chiao-Tung University http://www.cs.nctu.edu.tw/~cmliu/courses/dsp/ ( Office: EC538 (03)5731877 cmliu@cs.nctu.edu.tw
More informationNeuroimaging. BIE601 Advanced Biological Engineering Dr. Boonserm Kaewkamnerdpong Biological Engineering Program, KMUTT. Human Brain Mapping
11/8/2013 Neuroimaging N i i BIE601 Advanced Biological Engineering Dr. Boonserm Kaewkamnerdpong Biological Engineering Program, KMUTT 2 Human Brain Mapping H Human m n brain br in m mapping ppin can nb
More informationThe Tools: Imaging the Living Brain
The Tools: Imaging the Living Brain I believe the study of neuroimaging has supported the localization of mental operations within the human brain. -Michael I. Posner, 2003 Neuroimaging methods Since Descarte
More informationNeural Network based Heart Arrhythmia Detection and Classification from ECG Signal
Neural Network based Heart Arrhythmia Detection and Classification from ECG Signal 1 M. S. Aware, 2 V. V. Shete *Dept. of Electronics and Telecommunication, *MIT College Of Engineering, Pune Email: 1 mrunal_swapnil@yahoo.com,
More informationImpact of Signal-to-Noise on Functional MRI
Impact of Signal-to-Noise on Functional MRI Todd B. Parrish, 1,2,4 * Darren R. Gitelman, 1,3,4 Kevin S. LaBar, 3,4 and M.-Marsel Mesulam 3,4 Magnetic Resonance in Medicine 44:925 932 (2000) Functional
More informationPrediction of Successful Memory Encoding from fmri Data
Prediction of Successful Memory Encoding from fmri Data S.K. Balci 1, M.R. Sabuncu 1, J. Yoo 2, S.S. Ghosh 3, S. Whitfield-Gabrieli 2, J.D.E. Gabrieli 2 and P. Golland 1 1 CSAIL, MIT, Cambridge, MA, USA
More informationDevelopment of Ultrasound Based Techniques for Measuring Skeletal Muscle Motion
Development of Ultrasound Based Techniques for Measuring Skeletal Muscle Motion Jason Silver August 26, 2009 Presentation Outline Introduction Thesis Objectives Mathematical Model and Principles Methods
More informationFunctional Connectivity in the Motor Cortex of Resting Human Brain Using Echo-Planar MRI
Functional Connectivity in the Motor Cortex of Resting Human Brain Using Echo-Planar MRI Bharat Biswal, F. Zerrin Yetkin, Victor M. Haughton, James S. Hyde An MRI time course of 512 echo-planar images
More informationRemoving ECG Artifact from the Surface EMG Signal Using Adaptive Subtraction Technique
www.jbpe.org Removing ECG Artifact from the Surface EMG Signal Using Adaptive Subtraction Technique Original 1 Department of Biomedical Engineering, Amirkabir University of technology, Tehran, Iran Abbaspour
More informationThe neurolinguistic toolbox Jonathan R. Brennan. Introduction to Neurolinguistics, LSA2017 1
The neurolinguistic toolbox Jonathan R. Brennan Introduction to Neurolinguistics, LSA2017 1 Psycholinguistics / Neurolinguistics Happy Hour!!! Tuesdays 7/11, 7/18, 7/25 5:30-6:30 PM @ the Boone Center
More informationThe physiology of the BOLD signal What do we measure with fmri?
The physiology of the BOLD signal What do we measure with fmri? Methods and Models in fmri, 10.11.2012 Jakob Heinzle Translational Neuromodeling Unit (TNU) Institute for Biomedical Engineering (IBT) University
More informationHemodynamics and fmri Signals
Cerebral Blood Flow and Brain Activation UCLA NITP July 2011 Hemodynamics and fmri Signals Richard B. Buxton University of California, San Diego rbuxton@ucsd.edu... The subject to be observed lay on a
More informationGroup-Wise FMRI Activation Detection on Corresponding Cortical Landmarks
Group-Wise FMRI Activation Detection on Corresponding Cortical Landmarks Jinglei Lv 1,2, Dajiang Zhu 2, Xintao Hu 1, Xin Zhang 1,2, Tuo Zhang 1,2, Junwei Han 1, Lei Guo 1,2, and Tianming Liu 2 1 School
More informationApplying a Level Set Method for Resolving Physiologic Motions in Free-Breathing and Non-Gated Cardiac MRI
Applying a Level Set Method for Resolving Physiologic Motions in Free-Breathing and Non-Gated Cardiac MRI Ilyas Uyanik 1, Peggy Lindner 1, Panagiotis Tsiamyrtzis 2, Dipan Shah 3, Nikolaos V. Tsekos 1,
More informationDaniel Bulte. Centre for Functional Magnetic Resonance Imaging of the Brain. University of Oxford
Daniel Bulte Centre for Functional Magnetic Resonance Imaging of the Brain University of Oxford Overview Signal Sources BOLD Contrast Mechanism of MR signal change FMRI Modelling Scan design details Factors
More informationDeconvolution of Impulse Response in Event-Related BOLD fmri 1
NeuroImage 9, 416 429 (1999) Article ID nimg.1998.0419, available online at http://www.idealibrary.com on Deconvolution of Impulse Response in Event-Related BOLD fmri 1 Gary H. Glover Center for Advanced
More informationfmri: What Does It Measure?
fmri: What Does It Measure? Psychology 355: Cognitive Psychology Instructor: John Miyamoto 04/02/2018: Lecture 02-1 Note: This Powerpoint presentation may contain macros that I wrote to help me create
More informationDesign Optimization. Optimization?
Edinburgh 2015: biennial SPM course Design Optimization Cyril Pernet Centre for Clinical Brain Sciences (CCBS) Neuroimaging Sciences Optimization? Making a design that is the most favourable or desirable,
More informationCombining tdcs and fmri. OHMB Teaching Course, Hamburg June 8, Andrea Antal
Andrea Antal Department of Clinical Neurophysiology Georg-August University Goettingen Combining tdcs and fmri OHMB Teaching Course, Hamburg June 8, 2014 Classical Biomarkers for measuring human neuroplasticity
More informationULTRASOUND IMAGING EE 472 F2018. Prof. Yasser Mostafa Kadah
ULTRASOUND IMAGING EE 472 F2018 Prof. Yasser Mostafa Kadah www.k-space.org Recommended Textbook Diagnostic Ultrasound: Physics and Equipment, 2nd ed., by Peter R. Hoskins (Editor), Kevin Martin (Editor),
More informationExperimental Design and the Relative Sensitivity of BOLD and Perfusion fmri
NeuroImage 15, 488 500 (2002) doi:10.1006/nimg.2001.0990, available online at http://www.idealibrary.com on Experimental Design and the Relative Sensitivity of BOLD and Perfusion fmri G. K. Aguirre,*,1
More informationPerfusion-Based fmri. Thomas T. Liu Center for Functional MRI University of California San Diego May 7, Goal
Perfusion-Based fmri Thomas T. Liu Center for Functional MRI University of California San Diego May 7, 2006 Goal To provide a basic understanding of the theory and application of arterial spin labeling
More informationSpeech recognition in noisy environments: A survey
T-61.182 Robustness in Language and Speech Processing Speech recognition in noisy environments: A survey Yifan Gong presented by Tapani Raiko Feb 20, 2003 About the Paper Article published in Speech Communication
More informationGabor Wavelet Approach for Automatic Brain Tumor Detection
Gabor Wavelet Approach for Automatic Brain Tumor Detection Akshay M. Malviya 1, Prof. Atul S. Joshi 2 1 M.E. Student, 2 Associate Professor, Department of Electronics and Tele-communication, Sipna college
More informationAssessment of Reliability of Hamilton-Tompkins Algorithm to ECG Parameter Detection
Proceedings of the 2012 International Conference on Industrial Engineering and Operations Management Istanbul, Turkey, July 3 6, 2012 Assessment of Reliability of Hamilton-Tompkins Algorithm to ECG Parameter
More informationPhysiological Noise Reduction Using Volumetric Functional Magnetic Resonance Inverse Imaging
r Human Brain Mapping 0000:00 00 (2011) r Physiological Noise Reduction Using Volumetric Functional Magnetic Resonance Inverse Imaging Fa-Hsuan Lin, 1,2 Aapo Nummenmaa, 2,3 Thomas Witzel, 4 Jonathan R.
More informationUSING AUDITORY SALIENCY TO UNDERSTAND COMPLEX AUDITORY SCENES
USING AUDITORY SALIENCY TO UNDERSTAND COMPLEX AUDITORY SCENES Varinthira Duangudom and David V Anderson School of Electrical and Computer Engineering, Georgia Institute of Technology Atlanta, GA 30332
More informationAdvanced Data Modelling & Inference
Edinburgh 2015: biennial SPM course Advanced Data Modelling & Inference Cyril Pernet Centre for Clinical Brain Sciences (CCBS) Neuroimaging Sciences Modelling? Y = XB + e here is my model B might be biased
More informationNoise-Robust Speech Recognition in a Car Environment Based on the Acoustic Features of Car Interior Noise
4 Special Issue Speech-Based Interfaces in Vehicles Research Report Noise-Robust Speech Recognition in a Car Environment Based on the Acoustic Features of Car Interior Noise Hiroyuki Hoshino Abstract This
More informationExtraction of Unwanted Noise in Electrocardiogram (ECG) Signals Using Discrete Wavelet Transformation
Extraction of Unwanted Noise in Electrocardiogram (ECG) Signals Using Discrete Wavelet Transformation Er. Manpreet Kaur 1, Er. Gagandeep Kaur 2 M.Tech (CSE), RIMT Institute of Engineering & Technology,
More informationFunctional Magnetic Resonance Imaging with Arterial Spin Labeling: Techniques and Potential Clinical and Research Applications
pissn 2384-1095 eissn 2384-1109 imri 2017;21:91-96 https://doi.org/10.13104/imri.2017.21.2.91 Functional Magnetic Resonance Imaging with Arterial Spin Labeling: Techniques and Potential Clinical and Research
More informationMR Advance Techniques. Vascular Imaging. Class II
MR Advance Techniques Vascular Imaging Class II 1 Vascular Imaging There are several methods that can be used to evaluate the cardiovascular systems with the use of MRI. MRI will aloud to evaluate morphology
More informationExtraction of the Hemodynamic Response Function and Parameter Estimation for the Two Gamma Difference Model
Extraction of the Hemodynamic Response Function and Parameter Estimation for the Two Gamma Difference Model Joel O Hair, Richard F. Gunst, William R. Schucany, and Wayne A. Woodward Department of Statistical
More informationPerformance Comparison of Speech Enhancement Algorithms Using Different Parameters
Performance Comparison of Speech Enhancement Algorithms Using Different Parameters Ambalika, Er. Sonia Saini Abstract In speech communication system, background noise degrades the information or speech
More informationComparing event-related and epoch analysis in blocked design fmri
Available online at www.sciencedirect.com R NeuroImage 18 (2003) 806 810 www.elsevier.com/locate/ynimg Technical Note Comparing event-related and epoch analysis in blocked design fmri Andrea Mechelli,
More informationBrain Tumor Detection Using Image Processing.
47 Brain Tumor Detection Using Image Processing. Prof. Mrs. Priya Charles, Mr. Shubham Tripathi, Mr.Abhishek Kumar Professor, Department Of E&TC,DYPIEMR,Akurdi,Pune, Student of BE(E&TC),DYPIEMR,Akurdi,Pune,
More informationQuick detection of QRS complexes and R-waves using a wavelet transform and K-means clustering
Bio-Medical Materials and Engineering 26 (2015) S1059 S1065 DOI 10.3233/BME-151402 IOS Press S1059 Quick detection of QRS complexes and R-waves using a wavelet transform and K-means clustering Yong Xia
More informationBRAIN TUMOR DETECTION AND SEGMENTATION USING WATERSHED SEGMENTATION AND MORPHOLOGICAL OPERATION
BRAIN TUMOR DETECTION AND SEGMENTATION USING WATERSHED SEGMENTATION AND MORPHOLOGICAL OPERATION Swe Zin Oo 1, Aung Soe Khaing 2 1 Demonstrator, Department of Electronic Engineering, Mandalay Technological
More informationDevelopment of novel algorithm by combining Wavelet based Enhanced Canny edge Detection and Adaptive Filtering Method for Human Emotion Recognition
International Journal of Engineering Research and Development e-issn: 2278-067X, p-issn: 2278-800X, www.ijerd.com Volume 12, Issue 9 (September 2016), PP.67-72 Development of novel algorithm by combining
More informationBiomedical Signal Processing
DSP : Biomedical Signal Processing What is it? Biomedical Signal Processing: Application of signal processing methods, such as filtering, Fourier transform, spectral estimation and wavelet transform, to
More informationClustering of MRI Images of Brain for the Detection of Brain Tumor Using Pixel Density Self Organizing Map (SOM)
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 19, Issue 6, Ver. I (Nov.- Dec. 2017), PP 56-61 www.iosrjournals.org Clustering of MRI Images of Brain for the
More informationBRAIN STATE CHANGE DETECTION VIA FIBER-CENTERED FUNCTIONAL CONNECTIVITY ANALYSIS
BRAIN STATE CHANGE DETECTION VIA FIBER-CENTERED FUNCTIONAL CONNECTIVITY ANALYSIS Chulwoo Lim 1, Xiang Li 1, Kaiming Li 1, 2, Lei Guo 2, Tianming Liu 1 1 Department of Computer Science and Bioimaging Research
More informationDetection of Cognitive States from fmri data using Machine Learning Techniques
Detection of Cognitive States from fmri data using Machine Learning Techniques Vishwajeet Singh, K.P. Miyapuram, Raju S. Bapi* University of Hyderabad Computational Intelligence Lab, Department of Computer
More informationAn Unified Approach for fmri-measurements used by a New Real-Time fmri Analysis System
An Unified Approach for fmri-measurements used by a New Real-Time fmri Analysis System Maurice Hollmann 1, Tobias Moench 1, Claus Tempelmann 2, Johannes Bernarding 1 1 Institute for Biometry and Medical
More informationExploratory Identification of Cardiac Noise in fmri Images
Exploratory Identification of Cardiac Noise in fmri Images Lilla Zöllei 1, Lawrence Panych 2, Eric Grimson 1, and William M. Wells III 1,2 1 Massachusetts Institute of Technology, Artificial Intelligence
More informationDefine functional MRI. Briefly describe fmri image acquisition. Discuss relative functional neuroanatomy. Review clinical applications.
Dr. Peter J. Fiester November 14, 2012 Define functional MRI. Briefly describe fmri image acquisition. Discuss relative functional neuroanatomy. Review clinical applications. Briefly discuss a few examples
More informationTowards natural human computer interaction in BCI
Towards natural human computer interaction in BCI Ian Daly 1 (Student) and Slawomir J Nasuto 1 and Kevin Warwick 1 Abstract. BCI systems require correct classification of signals interpreted from the brain
More informationMEDICAL REVIEW Functional Magnetic Resonance Imaging: From Acquisition to Application
Functional Magnetic Resonance Imaging: From Acquisition to Application Gail Yarmish * and Michael L. Lipton *, Departments of Radiology *, and Neuroscience Albert Einstein College of Medicine Bronx, New
More informationExtraction of Blood Vessels and Recognition of Bifurcation Points in Retinal Fundus Image
International Journal of Research Studies in Science, Engineering and Technology Volume 1, Issue 5, August 2014, PP 1-7 ISSN 2349-4751 (Print) & ISSN 2349-476X (Online) Extraction of Blood Vessels and
More informationSupporting Online Material for
www.sciencemag.org/cgi/content/full/324/5927/646/dc1 Supporting Online Material for Self-Control in Decision-Making Involves Modulation of the vmpfc Valuation System Todd A. Hare,* Colin F. Camerer, Antonio
More informationHemodynamics and fmri Signals
Cerebral Blood Flow and Brain Activation UCLA NITP July 2010 Hemodynamics and fmri Signals Richard B. Buxton University of California, San Diego rbuxton@ucsd.edu... The subject to be observed lay on a
More informationExperimental design for Cognitive fmri
Experimental design for Cognitive fmri Alexa Morcom Edinburgh SPM course 2017 Thanks to Rik Henson, Thomas Wolbers, Jody Culham, and the SPM authors for slides Overview Categorical designs Factorial designs
More informationLATERAL INHIBITION MECHANISM IN COMPUTATIONAL AUDITORY MODEL AND IT'S APPLICATION IN ROBUST SPEECH RECOGNITION
LATERAL INHIBITION MECHANISM IN COMPUTATIONAL AUDITORY MODEL AND IT'S APPLICATION IN ROBUST SPEECH RECOGNITION Lu Xugang Li Gang Wang Lip0 Nanyang Technological University, School of EEE, Workstation Resource
More informationQ-Sense fmri in the MRI environment
Q-Sense fmri in the MRI environment An evaluation at the Erwin L. Hahn Institute for Magnetic Resonance Imaging, and at the Institute of Diagnostic and Interventional Radiology and Neuroradiology, Essen
More informationMammography is a most effective imaging modality in early breast cancer detection. The radiographs are searched for signs of abnormality by expert
Abstract Methodologies for early detection of breast cancer still remain an open problem in the Research community. Breast cancer continues to be a significant problem in the contemporary world. Nearly
More informationInformation Processing During Transient Responses in the Crayfish Visual System
Information Processing During Transient Responses in the Crayfish Visual System Christopher J. Rozell, Don. H. Johnson and Raymon M. Glantz Department of Electrical & Computer Engineering Department of
More informationFeasibility Evaluation of a Novel Ultrasonic Method for Prosthetic Control ECE-492/3 Senior Design Project Fall 2011
Feasibility Evaluation of a Novel Ultrasonic Method for Prosthetic Control ECE-492/3 Senior Design Project Fall 2011 Electrical and Computer Engineering Department Volgenau School of Engineering George
More informationAutomated Brain Tumor Segmentation Using Region Growing Algorithm by Extracting Feature
Automated Brain Tumor Segmentation Using Region Growing Algorithm by Extracting Feature Shraddha P. Dhumal 1, Ashwini S Gaikwad 2 1 Shraddha P. Dhumal 2 Ashwini S. Gaikwad ABSTRACT In this paper, we propose
More informationEdge Detection Techniques Using Fuzzy Logic
Edge Detection Techniques Using Fuzzy Logic Essa Anas Digital Signal & Image Processing University Of Central Lancashire UCLAN Lancashire, UK eanas@uclan.a.uk Abstract This article reviews and discusses
More information